Practices in radiation oncology quality assurance rounds: A scoping review protocol
Bibliographic record
Abstract
In radiation oncology (RO), quality assurance (QA) rounds are conducted regularly. During these rounds, charts are reviewed with the goal of ensuring standards of care are met, strengthening communication on an allied health team, and monitoring for any potential deficits or areas of improvement. Barriers to effective QA rounds can include time and scheduling commitments, lengthy discussion periods, and lack of equal and consistent contributions from all allied health members present. Recent studies have examined the implementation of specific practices into QA rounds such as random insertion of realistic errors and Group Consensus Peer Review (Talcott et al. 2020; Duggar et al., 2018). This scoping review aims to (1) provide practicing radiation oncologists a general landscape of current published practices in QA rounds, and (2) inform an improvement study targeting gastrointestinal-focused (GI) RO at the University of Calgary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.158 | 0.107 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.055 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".